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A drug target slim: using gene ontology and gene ontology annotations to navigate protein-ligand target space in ChEMBL

Overview of attention for article published in Journal of Biomedical Semantics, September 2016
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  • In the top 25% of all research outputs scored by Altmetric
  • One of the highest-scoring outputs from this source (#10 of 368)
  • High Attention Score compared to outputs of the same age (90th percentile)
  • High Attention Score compared to outputs of the same age and source (91st percentile)

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2 news outlets
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7 X users
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1 Facebook page

Citations

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31 Dimensions

Readers on

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51 Mendeley
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3 CiteULike
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Title
A drug target slim: using gene ontology and gene ontology annotations to navigate protein-ligand target space in ChEMBL
Published in
Journal of Biomedical Semantics, September 2016
DOI 10.1186/s13326-016-0102-0
Pubmed ID
Authors

Prudence Mutowo, A. Patrícia Bento, Nathan Dedman, Anna Gaulton, Anne Hersey, Jane Lomax, John P. Overington

Abstract

The process of discovering new drugs is a lengthy, time-consuming and expensive process. Modern day drug discovery relies heavily on the rapid identification of novel 'targets', usually proteins that can be modulated by small molecule drugs to cure or minimise the effects of a disease. Of the 20,000 proteins currently reported as comprising the human proteome, just under a quarter of these can potentially be modulated by known small molecules Storing information in curated, actively maintained drug discovery databases can help researchers access current drug discovery information quickly. However with the increase in the amount of data generated from both experimental and in silico efforts, databases can become very large very quickly and information retrieval from them can become a challenge. The development of database tools that facilitate rapid information retrieval is important to keep up with the growth of databases. We have developed a Gene Ontology-based navigation tool (Gene Ontology Tree) to help users retrieve biological information to single protein targets in the ChEMBL drug discovery database. 99 % of single protein targets in ChEMBL have at least one GO annotation associated with them. There are 12,500 GO terms associated to 6200 protein targets in the ChEMBL database resulting in a total of 140,000 annotations. The slim we have created, the 'ChEMBL protein target slim' allows broad categorisation of the biology of 90 % of the protein targets using just 300 high level, informative GO terms. We used the GO slim method of assigning fewer higher level GO groupings to numerous very specific lower level terms derived from the GOA to describe a set of GO terms relevant to proteins in ChEMBL. We then used the slim created to provide a web based tool that allows a quick and easy navigation of protein target space. Terms from the GO are used to capture information on protein molecular function, biological process and subcellular localisations. The ChEMBL database also provides compound information for small molecules that have been tested for their effects on these protein targets. The 'ChEMBL protein target slim' provides a means of firstly describing the biology of protein drug targets and secondly allows users to easily establish a connection between biological and chemical information regarding drugs and drug targets in ChEMBL. The 'ChEMBL protein target slim' is available as a browsable 'Gene Ontology Tree' on the ChEMBL site under the browse targets tab ( https://www.ebi.ac.uk/chembl/target/browser ). A ChEMBL protein target slim OBO file containing the GO slim terms pertinent to ChEMBL is available from the GOC website ( http://geneontology.org/page/go-slim-and-subset-guide ). We have created a protein target navigation tool based on the 'ChEMBL protein target slim'. The 'ChEMBL protein target slim' provides a way of browsing protein targets in ChEMBL using high level GO terms that describe the molecular functions, processes and subcellular localisations of protein drug targets in drug discovery. The tool also allows user to establish a link between ontological groupings representing protein target biology to relevant compound information in ChEMBL. We have demonstrated by the use of a simple example how the 'ChEMBL protein target slim' can be used to link biological processes with drug information based on the information in the ChEMBL database. The tool has potential to aid in areas of drug discovery such as drug repurposing studies or drug-disease-protein pathways.

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X Demographics

The data shown below were collected from the profiles of 7 X users who shared this research output. Click here to find out more about how the information was compiled.
Mendeley readers

Mendeley readers

The data shown below were compiled from readership statistics for 51 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
United Kingdom 1 2%
Netherlands 1 2%
Unknown 49 96%

Demographic breakdown

Readers by professional status Count As %
Researcher 15 29%
Student > Ph. D. Student 9 18%
Student > Master 7 14%
Student > Bachelor 4 8%
Other 3 6%
Other 4 8%
Unknown 9 18%
Readers by discipline Count As %
Computer Science 10 20%
Agricultural and Biological Sciences 9 18%
Biochemistry, Genetics and Molecular Biology 7 14%
Pharmacology, Toxicology and Pharmaceutical Science 3 6%
Engineering 3 6%
Other 6 12%
Unknown 13 25%
Attention Score in Context

Attention Score in Context

This research output has an Altmetric Attention Score of 21. This is our high-level measure of the quality and quantity of online attention that it has received. This Attention Score, as well as the ranking and number of research outputs shown below, was calculated when the research output was last mentioned on 04 April 2024.
All research outputs
#1,789,544
of 25,637,545 outputs
Outputs from Journal of Biomedical Semantics
#10
of 368 outputs
Outputs of similar age
#30,869
of 331,608 outputs
Outputs of similar age from Journal of Biomedical Semantics
#1
of 12 outputs
Altmetric has tracked 25,637,545 research outputs across all sources so far. Compared to these this one has done particularly well and is in the 93rd percentile: it's in the top 10% of all research outputs ever tracked by Altmetric.
So far Altmetric has tracked 368 research outputs from this source. They receive a mean Attention Score of 4.6. This one has done particularly well, scoring higher than 97% of its peers.
Older research outputs will score higher simply because they've had more time to accumulate mentions. To account for age we can compare this Altmetric Attention Score to the 331,608 tracked outputs that were published within six weeks on either side of this one in any source. This one has done particularly well, scoring higher than 90% of its contemporaries.
We're also able to compare this research output to 12 others from the same source and published within six weeks on either side of this one. This one has done particularly well, scoring higher than 91% of its contemporaries.